Original research · 2026-07 edition

AI SEO Statistics: Assisted Living (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in assisted living.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

When is it time to move a parent with early dementia into assisted living vs keeping them at home?
What's the average monthly cost for a one-bedroom assisted living apartment in a mid-sized city?
Are there assisted living facilities that allow small dogs or cats?
What specific medical services are typically included in the base monthly fee?
How do I know if my mom needs assisted living or just a part-time home health aide?
What are the biggest red flags to look for during an unannounced tour of a senior living community?
Does Medicare pay for any part of assisted living or is it all out of pocket?
What is the difference between level 1 and level 3 care in an assisted living contract?
Show all 40 questions
How do I handle a parent who refuses to move but isn't safe living alone anymore?
Can I use a long-term care insurance policy to cover the costs of a residential care facility?
What is the staff-to-resident ratio during the night shift in most assisted living homes?
What happens if my dad's health declines and he needs more help than the facility provides?
Are there assisted living options specifically for veterans that offer financial assistance?
How often do resident care plans get updated and who is involved in that meeting?
What kind of social activities are usually offered for residents who are still physically active?
Is it better to choose a large corporate-owned facility or a small family-run residential care home?
What are the move-in fees or community fees I should expect to pay upfront?
How do facilities handle medication management and is there an extra charge for it?
What should I ask current residents' families when I'm vetting a potential home?
Can my parents stay together in the same unit if only one of them needs daily assistance?
What are the rules for visiting hours and can family members stay overnight if needed?
How do I transition my mom from a hospital rehab stay directly into assisted living?
What kind of transportation services are provided for doctor appointments and grocery shopping?
Are meals served in a communal dining room or can residents eat in their own apartments?
What is the typical turnover rate for nursing staff and caregivers in this industry?
How does the facility handle emergency situations like falls or sudden illnesses at night?
Is there a way to lock in the monthly rate so it doesn't increase every year?
What should I look for in the fine print of an assisted living residency agreement?
Do most facilities provide furniture or do we need to bring everything from home?
How do they handle residents who wander or have a tendency to get lost?
What's the process for filing a complaint if we aren't happy with the level of care?
Are there specialized memory care wings within assisted living facilities for Alzheimer's patients?
Can we hire an outside private caregiver to come into the assisted living facility?
What are the most common hidden costs that aren't mentioned in the initial brochure?
How do I compare the quality ratings of different local assisted living homes?
What happens if a resident runs out of money—will they be evicted or is there a Medicaid bridge?
Are there religious-affiliated assisted living communities that offer specific spiritual services?
How is the food quality managed and can they accommodate strict diabetic or low-sodium diets?
What is the average waitlist time for a high-quality assisted living facility?
How do I talk to my siblings about splitting the cost of assisted living for our parents?

Model by model

17-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about assisted living buyers.

Behavior rates across 40 assisted living buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional40%33%18%63%
Suggests DIY first40%20%10%68%
Names specific providers10%10%5%83%
Gives price or cost info8%8%15%85%
Tells to check reviews3%8%3%88%
Tells to verify credentials13%5%5%85%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity25%13%5%65%
Gives selection criteria45%40%15%50%
Warns about red flags8%5%8%93%
Asks a clarifying question70%70%3%23%
Recommends multiple quotes10%3%3%88%

By model

How each assistant handled Assisted Living questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same assisted living questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 40% (ChatGPT) down to 17.5% (Gemini), a 23-point gap on an identical question set.

Across the 40 assisted living answers it produced, ChatGPT recommended hiring a professional in 40% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 7.5% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 12.5%, averaging 524 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 assisted living answers it produced, Claude recommended hiring a professional in 32.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 5%, averaging 282 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 assisted living answers it produced, Gemini recommended hiring a professional in 17.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 267 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route an assisted living buyer to a professional (40%) and Gemini the least (17.5%). ChatGPT produced the longest answers, at 524 words on average. Specific providers were named most often by ChatGPT (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 17.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an assisted living buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 70% (ChatGPT) — a 68-point spread.
  • Suggests a DIY approach first: from 10% (Gemini) to 40% (ChatGPT) — a 30-point spread.
  • Gives selection criteria: from 15% (Gemini) to 45% (ChatGPT) — a 30-point spread.
  • Recommends hiring a professional: from 17.5% (Gemini) to 40% (ChatGPT) — a 23-point spread.
  • Mentions local proximity: from 5% (Gemini) to 25% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 68 points — means an assisted living buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the assisted living market.

Where they agree

The points of near-consensus in Assisted Living.

On other behaviors the three models move almost in lockstep — the points of near-consensus for assisted living, where all three landed within a few points of each other:

  • Mentions case studies or portfolio: 0% across all three models.
  • Warns about red flags or scams: 5%–7.5% across all three (a 3-point spread).
  • Names a specific provider: 5%–10% across all three (a 5-point spread).
  • Tells the buyer to check reviews: 2.5%–7.5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (22.5%).

Every behavior, measured

All twelve coded behaviors for Assisted Living, averaged across the three models.

The behaviors AI models reproduce most often for assisted living are asks a clarifying question (47.5% on average), gives selection criteria (33.3%) and recommends hiring a professional (30%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (4.2%) and recommends multiple quotes (5%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Asks a clarifying question: 47.5% on average (ChatGPT 70%, Claude 70%, Gemini 2.5%) — a 68-point spread.
  • Gives selection criteria: 33.3% on average (ChatGPT 45%, Claude 40%, Gemini 15%) — a 30-point spread.
  • Recommends hiring a professional: 30% on average (ChatGPT 40%, Claude 32.5%, Gemini 17.5%) — a 23-point spread.
  • Suggests a DIY approach first: 23.3% on average (ChatGPT 40%, Claude 20%, Gemini 10%) — a 30-point spread.
  • Mentions local proximity: 14.2% on average (ChatGPT 25%, Claude 12.5%, Gemini 5%) — a 20-point spread.
  • Gives price or cost information: 10% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 15%) — a 8-point spread.
  • Names a specific provider: 8.3% on average (ChatGPT 10%, Claude 10%, Gemini 5%) — a 5-point spread.
  • Tells the buyer to verify credentials: 7.5% on average (ChatGPT 12.5%, Claude 5%, Gemini 5%) — a 8-point spread.
  • Warns about red flags or scams: 6.7% on average (ChatGPT 7.5%, Claude 5%, Gemini 7.5%) — a 3-point spread.
  • Recommends multiple quotes: 5% on average (ChatGPT 10%, Claude 2.5%, Gemini 2.5%) — a 8-point spread.
  • Tells the buyer to check reviews: 4.2% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the assisted living buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the assisted living buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.2% of answers on average. Verifying credentials or certifications appeared in 7.5%. Warning about red flags or scams appeared in 6.7%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 5%. The single least-reproduced protective signal for assisted living is "tells the buyer to check reviews" at 4.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Assisted Living providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 assisted living answers, a specific provider was named in 8.3% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for assisted living: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Assisted Living questions cover.

The 40 questions behind every percentage on this page were drawn from real assisted living (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact assisted living question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific assisted living question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

40 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-06, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →